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Published on: May 30, 2011
Evaluation of the effect of image noise on CT perfusion measurements using digital perfusion phantoms
Stephan Skornitzke1, Jessica Hirsch2, Hans-Ulrich Kauczor1
1Diagnostic & Interventional Radiology (DIR), Heidelberg University Hospital, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany.
Objectives:
To assess the influence of image noise on computed tomography (CT) perfusion studies, CT perfusion software algorithms were evaluated for susceptibility to image noise and results applied to clinical perfusion studies.
Methods:
Digital perfusion phantoms were generated using a published deconvolution model to create time-attenuation curves (TACs) for 16 different combinations of blood flow (BF; 30/60/90/120 ml/100 ml/min) and flow extraction product (FEP; 10/20/30/40 ml/100 ml/min) corresponding to values encountered in clinical studies. TACs were distorted with Gaussian noise at 50 different strengths to approximate image noise, performing 200 repetitions for each noise level. A total of 160,000 TACs were evaluated by measuring BF and FEP with CT perfusion software, comparing results for the maximum slope and Patlak models with those obtained with a deconvolution model. To translate results to clinical practice, data of 23 patients from a CT perfusion study were assessed for image noise, and the accuracy of reported CT perfusion measurements was estimated.
Results:
Perfusion measurements depend on image noise as means and standard deviations of BF and FEP over repetitions increase with increasing image noise, especially for low BF and FEP values. BF measurements derived by deconvolution show larger standard deviations than those performed with the maximum slope model. Image noise in the evaluated CT perfusion study was 26.46 ± 3.52 HU, indicating possible overestimation of BF by up to 85% in a clinical setting.
Conclusions:
Measurements of perfusion parameters depend heavily upon the magnitude of image noise, which has to be taken into account during selection of acquisition parameters and interpretation of results, e.g., as a quantitative imaging biomarker.
Key Points:
• CT perfusion results depend heavily upon the magnitude of image noise. • Different CT perfusion models react differently to the presence of image noise. • Blood flow may be overestimated by 85% in clinical CT perfusion studies.
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